code: def forward(self, img):
      device = img.device
      tokens = self.vit.to_patch_embedding(img)
      tokens = rearrange(tokens, "b ... d -> b (...) d")
  
      batch, num_patches, *_ = tokens.shape
  
      # Masking
      num_masked = int(self.masking_ratio * num_patches)
      rand_indices = torch.rand(
          batch, num_patches, device=device
      ).argsort(dim=-1)
      masked_indices, unmasked_indices = (
          rand_indices[:, :num_masked],
          rand_indices[:, num_masked:],
      )
  
      batch_range = torch.arange(batch, device=device)[:, None]
      tokens_unmasked = tokens[batch_range, unmasked_indices]
  
      attended_tokens = self.vit.transformer(tokens, tokens_unmasked)
      logits = rearrange(
          self.mlp_head(attended_tokens), "b n d -> (b n) d"
      )
  
      # Define labels
      labels = repeat(
          torch.arange(num_patches, device=device),
          "n -> (b n)",
          b=batch,
      )
      loss = F.cross_entropy(logits, labels)
  
      return loss
  
imports: import torch
  from torch import nn, einsum
  import torch.nn.functional
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange
label_desc: Use image node_id 2 for calling the MP3 obj's underlying member method code with example usage: obj.forward(img) and returns: loss